Optimization of slicing direction in laminated tooling for volume deviation reduction
Notice bibliographique
Résumé
Purpose Due to an uncertainty between actual model and assembled slices, there is always an extra material on assembled slices in laminated tooling. Therefore, a post processing, usually CNC machining, is required to remove this extra material and reach the near net shape surface for final product. One of the issues in laminated tooling is to minimize the amount of this extra material and reduce the cost of the post processing. Direction of slicing is an important parameter in this issue. This research aims to introduce a method to find the best slicing direction based on CAD model surface geometry and minimize the amount of the extra material in the assembled slices. Researches on the best slicing direction investigation so far were mostly based on the extra volume calculation for a number of candidate directions. Since the time needed for the extra volume calculation is proportionally high, the number of candidate directions to be investigated was usually limited, whereas, in the proposed method, the best slicing direction is found based on CAD model surface geometry and there is no need to find the actual amount of the extra volume. Moreover, the suggested method is developed to the cases where having more than one slicing direction is desirable for more reduction in the amount of the extra volume. The proposed optimization method can be used to find the best slicing direction in laminated tooling. Moreover, the ability to suggest multiple slicing directions can provide more reduction for the amount of the extra material. However, the number of candidate directions in the case of multiple slicing directions is limited due to joining problems in laminated tooling. Design/methodology/approach The investigation is based on the situation of normal vectors on CAD model surface. The CAD model surface is considered as a combination of planar tiles and all normal vectors of these tiles are considered as the candidate directions. This provides a number of candidates that can cover almost all possible slicing directions. The best slicing direction is then found by estimating the amount of the extra material produced on the tiles by each normal vector. Findings The proposed method applied to some examples. The case studies included the simple predictable models to qualify the reliability of the proposed method. Also more applicable examples were provided to show how the suggested method acts in real cases. Research limitations/implications The proposed method can be applied to each and every CAD model. Therefore, there is no limitation with regard to the type of model which can be investigated by the proposed method. However, there is limitation on the number of times the building direction can be changed in laminated tooling. Practical implications The proposed method can be employed to reduce the post processing time in laminated tooling. Originality/value Following the prior study researchers conducted in optimization of laminated dies, another parameter, slicing direction, is considered in this research. This brings a new approach on laminated dies optimization to reduce the production cost.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».